Publication

Exploratory Analysis of Endogeneity in Discrete Choice Models

Michel Bierlaire
2014
Conference Papers

Abstract

A model is said to be affected by endogeneity when its deterministic part is correlated with the error term. This is an issue that affects both linear models such as regression and non-linear models like discrete choice models. It is a classical and well studied problem in regression models, in particular in econometric literature. However, how to deal with endogeneity in discrete choice models is not as developed, and in particular still less has been done regarding endogeneity and transportation. The aim of this paper is to review what has already been developed in literature in order to identify potential contributions in this field. The paper contains a literature review and a description of three of the most common developed methods to deal with endogeneity.

Official source

https://infoscience.epfl.ch/entities/publication/8b13aaf5-ace9-48c7-add7-d0d94da17956

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Michel Bierlaire
2014
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8b13aaf5-ace9-48c7-add7-d0d94da17956

About this result

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

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Exploratory Analysis of Endogeneity in Discrete Choice Models

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